A Maximum Likelihood Framework for Integrating Taxonomies

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 20

Machine Learning

Many approaches have been proposed for the problem of mapping categories (classes)from a source taxonomy to classes in a master taxonomy. Most of these techniques, however, ignore the hierarchical structure of the taxonomies. In this paper, we propose a maximum likelihood based framework which exploits the hierarchical structure to obtain a more natural mapping between the source classes and the master taxonomy. Furthermore, unlike previous work, our technique also inserts source classes into appropriate places of the master hierarchy creating new categories if required. We evaluate our approach on text and hyperspectral datasets.